Ridge extraction to identify activated motor unit on heatmap generated by source localization of HD-sEMG signals
Résumé
This research explores the utilization of ridge extraction techniques for the identification of activated motor units from heat map images generated by source localization of high density surface electromyogram (HD-sEMG) signals. The primary objective of this research is to accurately determine the number of activated motor units in these images, facilitating enhanced understanding of neuromuscular activities. The study involves the application of two ridge detection approaches: the first employs Otsu's thresholding in conjunction with the watershed algorithm, while the second utilizes the Hessian matrix. By applying these methods, the aim is to enhance the accuracy of identifying ridges associated with activated motor units in the heat map images. Following ridge extraction, two methods are employed to estimate the number of motor units within the images. The first method employs the K-Nearest Neighbors (KNN) algorithm, demonstrating promising accuracy in determining the count of motor units. The second method involves the utilization of K-Means clustering; however, its performance is observed to be less consistent, especially when confronted with images containing more than two motor units. These findings underscore the importance of selecting appropriate analysis methods when working with complex neuromuscular data.
